Performance evaluation of algorithms for the State of Charge estimation of storage devices in microgrid operation

This paper analyzes different Kalman filtering algorithms for the real-time State of Charge (SoC) estimation of Battery Energy Storage System (BESS). Accurate SoC estimation is a key issue for microgrid real-time operation involving optimal model-based control. A BESS composed of Li-ion battery equipped with a Battery Management System (BMS) is characterized by fitting the parameters of a dynamic model, validated through experimental tests. Particular attention is devoted to the identification and representation of model nonlinearities in order to design robust Kalman filtering SoC estimation methods. Performance evaluation of the proposed algorithms are carried out by statistical simulations and experimental real-time tests. The analysis also takes in consideration the computational performances of the different methods in order to match the requirements of real-time control routines.

[1]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[2]  Mehdi Gholizadeh,et al.  Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model , 2014, IEEE Transactions on Industrial Electronics.

[3]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[4]  Raja Ayyanar,et al.  Design and Strategy for the Deployment of Energy Storage Systems in a Distribution Feeder With Penetration of Renewable Resources , 2015, IEEE Transactions on Sustainable Energy.

[5]  Michel Verhaegen,et al.  Filtering and System Identification: Frontmatter , 2007 .

[6]  T. Kim,et al.  A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects , 2011, IEEE Transactions on Energy Conversion.

[7]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[8]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[9]  V. Verdult,et al.  Filtering and System Identification: A Least Squares Approach , 2007 .

[10]  Federico Silvestro,et al.  Optimal Management Strategy of a Battery-Based Storage System to Improve Renewable Energy Integration in Distribution Networks , 2012, IEEE Transactions on Smart Grid.

[11]  ch vijay chandar,et al.  Coordinated Control and Energy Management of Distributed Generation Inverters in a Microgrid , 2016 .

[12]  S Julier,et al.  Comment on "A new method for the nonlinear transformation of means and covariances in filters and estimators" - Reply , 2002 .

[13]  Giovanni Fiengo,et al.  Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model , 2008, 2008 IEEE International Conference on Control Applications.

[14]  Sergio M. Savaresi,et al.  Kalman Filter SoC estimation for Li-Ion batteries , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[15]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[16]  Wei He,et al.  State of charge estimation for electric vehicle batteries using unscented kalman filtering , 2013, Microelectron. Reliab..